2-OnlineFraudPred-ModelBuilding.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b9b1ab44",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4720c99d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('online_payment.csv')\n",
    "del df['nameOrig']\n",
    "del df['nameDest']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aae78039",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['isFraud'],axis=1)\n",
    "y = df['isFraud']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "16eb97cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "var = X.select_dtypes(include='object').columns\n",
    "for i in var:\n",
    "    X[i] = le.fit_transform(X[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8c263655",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6362620 entries, 0 to 6362619\n",
      "Data columns (total 8 columns):\n",
      " #   Column          Dtype  \n",
      "---  ------          -----  \n",
      " 0   step            int64  \n",
      " 1   type            int32  \n",
      " 2   amount          float64\n",
      " 3   oldbalanceOrg   float64\n",
      " 4   newbalanceOrig  float64\n",
      " 5   oldbalanceDest  float64\n",
      " 6   newbalanceDest  float64\n",
      " 7   isFlaggedFraud  int64  \n",
      "dtypes: float64(5), int32(1), int64(2)\n",
      "memory usage: 364.1 MB\n"
     ]
    }
   ],
   "source": [
    "X.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c586c692",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = xgb.XGBClassifier(tree_method='gpu_hist', gpu_id=0,learning_rate=0.5,max_depth=6,sampling_method='gradient_based',max_bin=768,min_child_weight=1,gamma=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8b5f17a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train , X_test , y_train , y_test = train_test_split(X,y, test_size=0.30 , random_state=672,stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d101482a",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFlaggedFraud</th>\n",
       "    </tr>\n",
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      "text/plain": [
       "         step  type      amount  oldbalanceOrg  newbalanceOrig  \\\n",
       "689599     36     1    12660.25      106731.00        94070.75   \n",
       "1730708   161     3     9264.67           0.00            0.00   \n",
       "4199299   305     4  3779593.79       11583.00            0.00   \n",
       "24628       8     3      151.79      531396.45       531244.66   \n",
       "6098363   522     3       60.32           0.00            0.00   \n",
       "\n",
       "         oldbalanceDest  newbalanceDest  isFlaggedFraud  \n",
       "689599         13600.49        26260.74               0  \n",
       "1730708            0.00            0.00               0  \n",
       "4199299       758843.02      4538436.81               0  \n",
       "24628              0.00            0.00               0  \n",
       "6098363            0.00            0.00               0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "afe9a257",
   "metadata": {},
   "outputs": [
    {
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       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, gamma=0.2, gpu_id=0, grow_policy=&#x27;depthwise&#x27;,\n",
       "              importance_type=None, interaction_constraints=&#x27;&#x27;,\n",
       "              learning_rate=0.5, max_bin=768, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "              missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=100,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, gamma=0.2, gpu_id=0, grow_policy=&#x27;depthwise&#x27;,\n",
       "              importance_type=None, interaction_constraints=&#x27;&#x27;,\n",
       "              learning_rate=0.5, max_bin=768, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "              missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=100,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, gamma=0.2, gpu_id=0, grow_policy='depthwise',\n",
       "              importance_type=None, interaction_constraints='',\n",
       "              learning_rate=0.5, max_bin=768, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "              missing=nan, monotone_constraints='()', n_estimators=100,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, ...)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9425cec6",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7b4da054",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1908786, 8)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "95e2ad3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00   1906322\n",
      "           1       0.97      0.89      0.93      2464\n",
      "\n",
      "    accuracy                           1.00   1908786\n",
      "   macro avg       0.98      0.94      0.96   1908786\n",
      "weighted avg       1.00      1.00      1.00   1908786\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c171f6cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  confusion matrix  \n",
      "\n",
      "[[1906252      70]\n",
      " [    279    2185]]\n"
     ]
    }
   ],
   "source": [
    "print('  confusion matrix  ')\n",
    "print('')\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "362980cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy:  0.9999250084309383\n",
      "Test Accuracy:  0.999817161274234\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Accuracy: \",model.score(X_train,y_train))\n",
    "print(\"Test Accuracy: \",model.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "074f65c5",
   "metadata": {},
   "outputs": [
    {
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       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFlaggedFraud</th>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         step  type      amount  oldbalanceOrg  newbalanceOrig  \\\n",
       "689599     36     1    12660.25      106731.00        94070.75   \n",
       "1730708   161     3     9264.67           0.00            0.00   \n",
       "4199299   305     4  3779593.79       11583.00            0.00   \n",
       "24628       8     3      151.79      531396.45       531244.66   \n",
       "6098363   522     3       60.32           0.00            0.00   \n",
       "\n",
       "         oldbalanceDest  newbalanceDest  isFlaggedFraud  \n",
       "689599         13600.49        26260.74               0  \n",
       "1730708            0.00            0.00               0  \n",
       "4199299       758843.02      4538436.81               0  \n",
       "24628              0.00            0.00               0  \n",
       "6098363            0.00            0.00               0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd8e069c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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